Sklearn Pipeline Tutorial

Overview

This Jupyter notebook provides a quick tutorial on using the scikit-learn library for machine learning in Python. It combines discussions on the impact of hardware, specifically processing cores, with practical machine learning applications. Key sections include:

1. AI Generated Pipeline: An introduction into a Chatgpt-generated scikit-learn pipeline for logistic regression. This code is an example of a basic scikit-learn pipeline but greedily utilizes all cores which can cause issues on shared systems like the Yens.
2. Why Cores Matter: This section shows the significance of processing cores in machine learning, providing insights into computational efficiency and performance. A quick comparative study on the performance and implications of using 10 and 40 processing cores.
3. Predictions: This section shows one way you might perform inference with pre-trained pipelines
4. Scoring: A quick look into one way to evaluate these machine learning pipeline outputs

Prerequisites

To get the most out of this tutorial, you should have:

• Basic knowledge of Python programming.
• An understanding of fundamental machine learning concepts.
• Python 3.x installed on your machine.
• Jupyter Notebook or Jupyter Lab installed.

Installation

Before starting, ensure you have the following Python libraries installed:

• scikit-learn: for machine learning algorithms and tools.
• numpy: for numerical computations.
• pandas: for data manipulation and analysis.

You can install these packages using pip:

pip install scikit-learn numpy pandas

Running the Notebook

1. Clone or download this repository to your local machine or the Yens.
2. Navigate to the directory containing the notebook.
3. Open the Sklearn_Pipeline.ipynb file in the Jupyter interface.
4. Execute the cells in order to follow along with the tutorial.

Contributing

Contributions to this tutorial are welcome! If you have suggestions or improvements, feel free to submit a pull request or open an issue.